Coding with AI: My Honest Experience After 6 Months

The Day Everything Changed

I remember the exact moment. I was writing a function to parse CSV data, something I'd done dozens of times before. On a whim, I wrote a comment describing what I wanted, and the AI suggested the entire implementation.

It was correct. It handled edge cases I would have forgotten. It took 3 seconds.

That's when I knew coding was changing forever.

What AI Coding Actually Looks Like Day-to-Day

Let me be honest: AI coding isn't magic. It's more like having a very fast, sometimes brilliant, sometimes confused junior developer sitting next to you.

The Good Days

On good days, AI feels like a superpower:

• Writing boilerplate code in seconds
• Generating test cases I wouldn't have thought of
• Explaining unfamiliar codebases
• Converting code between languages
• Suggesting better approaches to problems

I once asked it to refactor a messy 200-line function. It broke it into clean, testable pieces with better naming than I would have chosen. That felt magical.

The Frustrating Days

But some days, AI feels like it's actively working against you:

• Confidently suggesting code that doesn't compile
• Using deprecated APIs from 3 years ago
• Getting stuck in loops of wrong suggestions
• Ignoring context you've already provided
• Making the same mistake repeatedly

I've learned that AI is not a replacement for understanding. It's a tool that amplifies what you already know.

How My Workflow Actually Changed

Before AI

1. Think about the problem
2. Google for solutions
3. Read Stack Overflow
4. Copy-paste and modify
5. Debug when it doesn't work

After AI

1. Think about the problem
2. Describe what I need to AI
3. Review the suggestion critically
4. Iterate with more specific prompts
5. Verify the logic actually makes sense

The time savings are real, but so is the mental shift. I spend less time typing and more time thinking and reviewing.

What AI Does Really Well

1. Boilerplate and Repetitive Code

Setting up a new Express server? Creating React components with TypeScript types? Writing database models? AI crushes these tasks.

2. Learning New Technologies

When I started learning Rust, AI became my patient tutor. It explained ownership, helped me understand lifetimes, and showed me idiomatic patterns. It was like having a mentor available 24/7.

3. Code Review and Improvements

"How can I make this code better?" turns out to be a powerful prompt. AI often suggests optimizations, security improvements, and cleaner patterns I wouldn't have considered.

4. Documentation and Comments

I hate writing documentation. AI doesn't. It generates clear, comprehensive docs from my code, and I just review and adjust.

5. Debugging Assistance

Paste an error message and your code, ask what's wrong. More often than not, AI spots the issue instantly.

What AI Struggles With

1. Complex Business Logic

AI doesn't understand your business. It doesn't know that "users" in your system have special permissions, or that certain operations need to happen in a specific order. You still need to provide that context.

2. Architecture Decisions

Should you use microservices or a monolith? Which database is right for your use case? AI can give generic advice, but these decisions need human judgment about your specific situation.

3. Up-to-Date Information

AI knowledge has a cutoff date. New libraries, recent API changes, latest best practices - you still need to stay current yourself.

4. Security-Critical Code

I never blindly trust AI for authentication, encryption, or anything security-related. It might suggest vulnerable patterns from outdated examples.

The Skills That Matter More Now

Ironically, AI has made certain human skills MORE valuable:

Asking Good Questions

Garbage in, garbage out. The better you describe what you need, the better results you get. Prompt engineering is a real skill.

Code Review Skills

You need to catch AI mistakes. Understanding code deeply enough to spot subtle bugs is crucial.

System Design

AI can write functions, but designing how systems fit together is still very human.

Communication

Explaining requirements, understanding user needs, working with teams - AI can't do any of this.

My Honest Productivity Assessment

Am I faster? Yes, probably 30-40% faster on average.

But it's not uniform:

• Simple CRUD operations: 70% faster
• Complex algorithms: 20% faster (AI helps, but I still need to think hard)
• Debugging weird issues: Sometimes faster, sometimes slower (AI can mislead)
• Learning new tech: 50% faster (AI as a tutor is amazing)
• Architecture and design: Minimal change (still need deep thinking)

The Future I'm Preparing For

AI coding tools are getting better fast. What I've learned in 6 months:

1. Embrace the tools - Refusing to use AI won't make it go away
2. Stay fundamentally strong - Understanding beats memorization
3. Focus on what AI can't do - Creativity, empathy, judgment
4. Keep learning - AI is a tool, not a replacement for growth

My Advice for Developers

If you're not using AI coding tools yet, start. But do it thoughtfully:

• Don't copy-paste blindly - understand what AI gives you
• Use it to learn, not to avoid learning
• Verify security-sensitive code carefully
• Remember that AI makes mistakes confidently
• Stay curious about the underlying concepts

Final Thoughts

Coding with AI isn't the end of programming. It's a new chapter.

Yes, some tasks are easier now. Yes, the barrier to entry is lower. But building great software still requires understanding, creativity, and good judgment.

AI is my coding partner now. It handles the tedious parts so I can focus on the interesting problems. That's not a threat to my career - it's an upgrade.

The developers who thrive will be the ones who use AI as a multiplier for their skills, not a replacement for thinking.

So embrace the change. Learn the tools. But never stop understanding the fundamentals.

The future belongs to developers who can work WITH AI, not against it.